Patterns of time since last meal revealed by sparse PCA in an observational LC–MS based metabolomics study

摘要: In metabolomics studies, liquid chromatography mass spectrometry (LC–MS) provides comprehensive information on biological samples. However, extraction of few relevant metabolites from this large and complex data is cumbersome. To resolve issue, we have employed sparse principal component analysis (SPCA) to capture the underlying patterns select LC–MS plasma profiles. The study involves a small pilot cohort with 270 subjects where each subject’s time since last meal (TSLM) has been recorded prior sampling. Our results demonstrated that both PCA SPCA can TSLM patterns. Nevertheless, more easily interpretable loadings in terms selection metabolites, which are identified as amino acids lyso-lipids. This demonstrates utility pattern recognition variable tool metabolomics. Furthermore, lyso-lipids determined dominating compounds response TSLM.